During my interview process for a Machine Learning Engineer role at Google, I went through multiple rounds:

1. Initial Recruiter Screen – The process started with a call from a recruiter. We discussed my background, what the role entails, and the overall interview structure.

2. Technical Phone Screen – Next, I had a 45-60 minute coding interview that focused on data structures, algorithms, and machine learning concepts. I used Python (C++ was also an option) to solve problems in real time.

3. Onsite Interviews (4-5 Rounds) – This was the most intensive part of the process:

   • Coding Interviews (2 rounds) – These tested my knowledge of data structures, algorithms, system design, and ML-related problem-solving.
   • ML System Design Interview – I was asked to design a scalable ML architecture and justify my choices.
   • Applied ML Interview – This round focused on my understanding of ML fundamentals, my ability to apply research, and how I solve real-world ML challenges.
   • Behavioral Interview – Here, I was evaluated on teamwork, leadership, and my general approach to problem-solving.

4. Hiring Committee Review – After the interviews, my performance was reviewed by a hiring committee, which made the final decision.

The process was rigorous, requiring strong algorithmic skills, deep ML knowledge, and expertise in system design.

Implement a function to sample from a multinomial distribution efficiently.